{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## 制作出可供训练的字迹样本图片\n","\n","1. 垫着带框的底纸, 在上面的白纸上, 按顺序写上文字\n","\n","<img src=\"./1-%E5%B8%A6%E6%A1%86%E5%AD%97%E8%BF%B9.jpeg\" alt=\"1-带框字迹\" height=\"400\"/>\n","\n","2. 去掉底纸或换上纯白底纸, 对写好字的纸拍照\n","\n","<img src=\"./2-%E6%97%A0%E6%A1%86%E5%AD%97%E8%BF%B9.jpeg\" alt=\"2-无框字迹\" height=\"400\"/>\n","\n","3. 在图片编辑软件里打开照片, 每隔100像素加一条参考线, 把图片按比例缩放和移动, 让每个字都在参考线框内, 然后切片输出包含所有字的大图\n","\n","<img src=\"./3-%E6%AF%94%E4%BE%8B%E7%BC%A9%E6%94%BE%E6%89%80%E6%9C%89%E5%AD%97%E5%9C%A8%E5%8F%82%E8%80%83%E7%BA%BF%E6%A1%86%E5%86%85%E5%86%8D%E5%88%87%E7%89%87.png\" alt=\"3-比例缩放所有字在参考线框内再切片\" height=\"400\"/>\n","\n","#### TL;DR\n","这只是一个笔者的字, 分辨笔迹作者训练还要让几十个笔者, 每人写同样的几百个字, 如果要识别文字, 要更多的笔者才能提高训练效果和识别率 "]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["挽\n"]},{"data":{"image/png":"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","text/plain":["<PIL.Image.Image 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","text/plain":["<PIL.Image.Image 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","text/plain":["<PIL.Image.Image 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","text/plain":["<PIL.Image.Image 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","text/plain":["<PIL.Image.Image 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","text/plain":["<PIL.Image.Image 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","text/plain":["<PIL.Image.Image image mode=RGBA size=100x100>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["<class 'torch.Tensor'>\n","闽\n"]},{"data":{"image/png":"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","text/plain":["<PIL.Image.Image image mode=RGBA size=100x100>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["<class 'torch.Tensor'>\n","CPU times: user 176 ms, sys: 26.2 ms, total: 202 ms\n","Wall time: 573 ms\n"]}],"source":["%%time\n","\n","from PIL import Image\n","import torchvision.transforms as T\n","from pathlib import Path\n","\n","convert_tensor = T.ToTensor()\n","\n","char_list = list('挽住云河洗天青闽山闽水物华青新小梅正吐黄金蕊老榕先掬碧玉心君驭南风冬亦暖我临东海情同深难得举城作一庆爱我人民爱我军秦时明月汉时关万里长征人未还但使龙城飞将在不教')\n","\n","big_img_path = '同比例缩放和裁剪后的所有字迹图.png'\n","\n","img = Image.open(big_img_path)\n","\n","# 未分割前的原图大小\n","# orig_w, orig_h = T.functional.get_image_size(img)\n","# print(orig_w, orig_h)\n","# img.show()\n","\n","crop_w_h = 100  # 每个字要切出的宽和高\n","i = 0\n","for row in range(10): # 原图有10行\n","    for col in range(8):    # 每行有8个字\n","        crop_img = T.functional.crop(img, row * crop_w_h, col * crop_w_h, crop_w_h, crop_w_h)\n","        print(char_list[i])\n","        crop_img.show()\n","\n","        # 把分割的单字保存到作者名目录下的单个图片文件(可选)\n","        Path(\"./作者名\").mkdir(parents=True, exist_ok=True)\n","        crop_img.save('./作者名/' + char_list[i] + '.png', quality=95)\n","\n","        # 或者不需要保存单字的图片, 直接转为张量放入数据样本集里开始处理和训练(可选)\n","        print(type(convert_tensor(crop_img)))\n","\n","        i += 1\n","    if i>5: # 只处理前几个字做示范, 正式时要全部字都处理\n","        break\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## 整理样本数据集\n","生成图像张量和图像增广"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["文件数: 28674\n","CPU times: user 115 ms, sys: 25.3 ms, total: 140 ms\n","Wall time: 2.75 s\n"]}],"source":["%%time\n","\n","from glob import glob\n","\n","PATH = './dataset/'\n","dataset_files = glob(f'{PATH}/*/*/*.*')\n","print('文件数:', len(dataset_files))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%%time\n","\n","%matplotlib inline\n","\n","import numpy as np\n","import pandas as pd\n","from tqdm import tqdm\n","import re\n","\n","import torch\n","import torchvision\n","from torch import nn\n","from d2l import torch as d2l\n","\n","d2l.set_figsize()\n","\n","def parse_file_name(dataset_files):\n","    dataset = []\n","    for fname in tqdm(dataset_files):\n","        fname2 = fname.lower()\n","        if not fname2.endswith('.png') and not fname2.endswith('.jpg'):\n","            # print(fname)\n","            continue # 不是图片类型的文件就跳过\n","        writerNfilename = fname2.split('/')\n","        characterList = re.findall('[\\u4e00-\\u9fa5]', writerNfilename[-1]) # 把文件名里的中文字提取出来\n","        if len(characterList)>0:\n","            img = d2l.Image.open(fname)\n","            orig_w, orig_h = torchvision.transforms.functional.get_image_size(img)\n","            if orig_w>500 or orig_h>500:\n","                continue # 跳过太大的没有做分割的原图\n","            dataset.append( [fname, writerNfilename[-2], characterList[0], orig_w, orig_h ] ) # 只要文件名的第一个中文字\n","\n","    return pd.DataFrame(dataset, columns = ['imagepath', 'writer', 'character', 'orig_w', 'orig_h']) # 原始文件路径,笔者,文字\n","\n","# 生成数据集\n","dataset = parse_file_name(dataset_files)\n","\n","# 保存带原始宽高的完整数据集\n","dataset.to_csv('./dataset/full_with_size.zip', index=False)\n","\n","# 读出已保存的带原始宽高数据集(全部)\n","dataset = pd.read_csv('./dataset/full_with_size.zip')\n","\n","dataset"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.preprocessing import LabelEncoder  \n","\n","# 对标签编码,方便计算目标函数\n","writer_le = LabelEncoder()\n","character_le = LabelEncoder()\n","\n","writer_label = writer_le.fit_transform(dataset.writer)\n","character_label = character_le.fit_transform(dataset.character)\n","\n","print(writer_label, writer_le.inverse_transform(writer_label), character_label, character_le.inverse_transform(character_label))\n","\n","dataset['writer_label'] = writer_label\n","dataset['character_label'] = character_label\n","\n","# print(dataset['character'].describe())\n","# print(dataset['character_label'].unique())\n","\n","# 可以通过映射找回原始标签\n","# print(character_le.inverse_transform([1]))\n","\n","dataset"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%%time\n","\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","\n","# 把10%的数据集分割出来,做训练后的验证数据,注意可能验证集y里有训练集y不存在的样本,就会导致该标签无法预测\n","train, test = train_test_split(dataset, test_size=0.1)\n","\n","# 验证集中的文字如果没有在训练集里有样本, 预测时也会出现 `y contains previously unseen labels`, 删除几个不存在 \n","train_char_unique = train['character'].unique()\n","print('验证集的原字数:', len(test))\n","test_char = test[(test['character'].isin(train_char_unique))]\n","print('将参与训练集的验证集字数:', len(test_char))\n","\n","# # 保存分割好的两个数据集\n","train.to_csv('./dataset/train.zip', index=False)\n","test_char.to_csv('./dataset/test.zip', index=False)\n","\n","# # # 读出已保存的训练和验证集\n","train = pd.read_csv('./dataset/train.zip')\n","test = pd.read_csv('./dataset/test.zip')\n","\n","train, test\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### 训练集图像增广"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%%time\n","\n","from PIL import Image\n","import torchvision.transforms as T\n","\n","# 训练集图像增广处理函数\n","data_transforms = T.Compose([\n","    T.Resize(size=28), # 按比调整短边为28px\n","    T.CenterCrop(size=28), # 中间截取28x28px\n","    T.Grayscale(),  # 转为灰度\n","    T.ToTensor()\n","])\n","\n","train_img_tensor = []\n","for _, row in tqdm(train.iterrows(), total=train.shape[0]):\n","    train_img_tensor.append( data_transforms( Image.open(row[0]) ) )\n","\n","train_angle0 = train.copy(deep=True)\n","train_angle0['img_tensor'] = train_img_tensor\n","\n","print(train_angle0.head())\n","\n","# 旋转出3个方向样本集\n","train_img_angle90_tensor = []\n","for _, row in tqdm(train_angle0.iterrows(), total=train_angle0.shape[0]):\n","    train_img_angle90_tensor.append( T.functional.rotate( img=row[-1], angle=90) )\n","\n","train_angle90 = train.copy(deep=True)\n","train_angle90['img_tensor'] = train_img_angle90_tensor\n","\n","print(train_angle90.head())\n","\n","train_img_angle180_tensor = []\n","for _, row in tqdm(train_angle0.iterrows(), total=train_angle0.shape[0]):\n","    train_img_angle180_tensor.append( T.functional.rotate( img=row[-1], angle=180) ) \n","\n","train_angle180 = train.copy(deep=True)\n","train_angle180['img_tensor'] = train_img_angle180_tensor\n","\n","print(train_angle180.head())\n","\n","train_img_angle270_tensor = []\n","for _, row in tqdm(train_angle0.iterrows(), total=train_angle0.shape[0]):\n","    train_img_angle270_tensor.append( T.functional.rotate( img=row[-1], angle=270) ) \n","\n","train_angle270 = train.copy(deep=True)\n","train_angle270['img_tensor'] = train_img_angle270_tensor\n","\n","print(train_angle270.head())\n","\n","# 合并成更大的训练样本集\n","train_big_list = [train_angle0,train_angle90,train_angle180,train_angle270]\n","train_big = pd.concat(train_big_list)\n","\n","train_big.sort_index(inplace=True)\n","train_big.reset_index(inplace = True)\n","train_big.drop(columns=['index'], inplace = True)\n","\n","train_big\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%%time\n","\n","# 验证数据集也要做简单的图像增广，不旋转\n","test_img_tensor = []\n","for _, row in tqdm(test.iterrows(), total=test.shape[0]):\n","    test_img_tensor.append( data_transforms( Image.open(row[0]) ) )\n","\n","test['img_tensor'] = test_img_tensor\n","\n","test"]},{"cell_type":"code","execution_count":111,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["['从', '着', '德', '种', '一']\n","CPU times: user 214 ms, sys: 489 ms, total: 702 ms\n","Wall time: 142 ms\n"]},{"data":{"image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" 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row_title=None, **imshow_kwargs):\n","    if not isinstance(imgs[0], list):\n","        # Make a 2d grid even if there's just 1 row\n","        imgs = [imgs]\n","\n","    num_rows = len(imgs)\n","    num_cols = len(imgs[0]) + with_orig\n","    fig, axs = plt.subplots(nrows=num_rows, ncols=num_cols, squeeze=False)\n","    for row_idx, row in enumerate(imgs):\n","        row = [orig_img] + row if with_orig else row\n","        for col_idx, img in enumerate(row):\n","            ax = axs[row_idx, col_idx]\n","            if torch.is_tensor(img):\n","                # ax.imshow(np.transpose(img.numpy(), (1,2,0)), **imshow_kwargs)\n","                ax.imshow(img.permute(1,2,0), **imshow_kwargs)\n","            else:\n","                ax.imshow(np.asarray(img), **imshow_kwargs)\n","            ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])\n","\n","    if with_orig:\n","        axs[0, 0].set(title='Original image')\n","        axs[0, 0].title.set_size(8)\n","    if row_title is not None:\n","        for row_idx in range(num_rows):\n","            axs[row_idx, 0].set(ylabel=row_title[row_idx])\n","\n","    plt.tight_layout()\n","\n","# print(train_big[103010:103018]['character'].tolist())\n","# plot(train_big[103010:103018]['img_tensor'].tolist(), cmap='gray')\n","\n","print(test[:5]['character'].tolist())\n","plot(test[:5]['img_tensor'].tolist(), cmap='gray')"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## 用CPU, 一个和两个全连接层, 训练分辨笔迹作者\n","这里只做分辨笔迹作者, 因为样本少, CPU的TFLOPS慢, 文字识别训练效果更差...就不折腾了"]},{"cell_type":"code","execution_count":229,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["加载线程(CPU核数): 8\n"]}],"source":["# 转换训练集和验证集到DataLoaders\n","from torch.utils.data import DataLoader\n","import os\n","num_workers = os.cpu_count()\n","print('加载线程(CPU核数):', num_workers)\n","\n","def load_writer_data(batch_size):\n","    \"\"\"数据集其加载到内存中\"\"\"\n","    train_big_writer_list = train_big[['img_tensor', 'writer_label']].values.tolist()\n","    test_writer_list = test[['img_tensor', 'writer_label']].values.tolist()\n","\n","    return (DataLoader(dataset=train_big_writer_list, \n","            batch_size=batch_size, # how many samples per batch?\n","            num_workers=num_workers, # how many subprocesses to use for data loading? (higher = more)\n","            shuffle=True),\n","            DataLoader(dataset=test_writer_list, \n","            batch_size=batch_size, \n","            num_workers=num_workers, \n","            shuffle=True))\n","\n","batch_size = 256\n","writer_train_iter, writer_test_iter = load_writer_data(batch_size)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%matplotlib inline\n","# 单个全连接层\n","\n","import torch\n","from torch import nn\n","from d2l import torch as d2l\n","from IPython import display\n","\n","d2l.use_svg_display()\n","\n","num_inputs = (28*28)\n","num_outputs = len(dataset['writer_label'].unique())\n","\n","W = torch.normal(0, 0.01, size=(num_inputs, num_outputs), requires_grad=True)\n","b = torch.zeros(num_outputs, requires_grad=True)\n","\n","# PyTorch不会隐式地调整输入的形状。因此，\n","# 我们在线性层前定义了展平层（flatten），来调整网络输入的形状\n","net = nn.Sequential(nn.Flatten(), nn.Linear(num_inputs, num_outputs))\n","\n","def init_weights(m):\n","    if type(m) == nn.Linear:\n","        nn.init.normal_(m.weight, std=0.01)\n","\n","net.apply(init_weights);\n","\n","loss = nn.CrossEntropyLoss(reduction='none')\n","\n","trainer = torch.optim.SGD(net.parameters(), lr=0.1)\n"]},{"cell_type":"code","execution_count":253,"metadata":{},"outputs":[],"source":["%matplotlib inline\n","# 两个全连接层\n","\n","import torch\n","from torch import nn\n","from d2l import torch as d2l\n","from IPython import display\n","\n","d2l.use_svg_display()\n","\n","num_inputs = (28*28)\n","hidden_units = len(dataset['writer_label'].unique())\n","num_outputs = len(dataset['writer_label'].unique())\n","\n","W = torch.normal(0, 0.01, size=(num_inputs, num_outputs), requires_grad=True)\n","b = torch.zeros(num_outputs, requires_grad=True)\n","\n","# PyTorch不会隐式地调整输入的形状。因此，\n","# 我们在线性层前定义了展平层（flatten），来调整网络输入的形状\n","net = nn.Sequential(nn.Flatten(), \n","            nn.Linear(in_features=num_inputs, out_features=hidden_units),\n","            nn.ReLU(),\n","            nn.Linear(in_features=hidden_units, out_features=num_outputs),\n","            nn.ReLU())\n","\n","def init_weights(m):\n","    if type(m) == nn.Linear:\n","        nn.init.normal_(m.weight, std=0.01)\n","\n","net.apply(init_weights);\n","\n","loss = nn.CrossEntropyLoss(reduction='none')\n","\n","trainer = torch.optim.SGD(net.parameters(), lr=0.1)\n"]},{"cell_type":"code","execution_count":221,"metadata":{},"outputs":[],"source":["class Accumulator:\n","    \"\"\"在n个变量上累加\"\"\"\n","    def __init__(self, n):\n","        self.data = [0.0] * n\n","\n","    def add(self, *args):\n","        self.data = [a + float(b) for a, b in zip(self.data, args)]\n","\n","    def reset(self):\n","        self.data = [0.0] * len(self.data)\n","\n","    def __getitem__(self, idx):\n","        return self.data[idx]"]},{"cell_type":"code","execution_count":222,"metadata":{},"outputs":[],"source":["def train_epoch_ch3(net, train_iter, loss, updater):\n","    \"\"\"训练模型一个迭代周期（定义见第3章https://zh.d2l.ai/chapter_linear-networks/index.html）\"\"\"\n","    # 将模型设置为训练模式\n","    if isinstance(net, torch.nn.Module):\n","        net.train()\n","    # 训练损失总和、训练准确度总和、样本数\n","    metric = Accumulator(3)\n","    for X, y in train_iter:\n","        # 计算梯度并更新参数\n","        y_hat = net(X)\n","        l = loss(y_hat, y)\n","        if isinstance(updater, torch.optim.Optimizer):\n","            # 使用PyTorch内置的优化器和损失函数\n","            updater.zero_grad()\n","            l.mean().backward()\n","            updater.step()\n","        else:\n","            # 使用定制的优化器和损失函数\n","            l.sum().backward()\n","            updater(X.shape[0])\n","        metric.add(float(l.sum()), accuracy(y_hat, y), y.numel())\n","    # 返回训练损失和训练精度\n","    return metric[0] / metric[2], metric[1] / metric[2]\n","\n","def accuracy(y_hat, y):\n","    \"\"\"计算预测正确的数量\"\"\"\n","    if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n","        y_hat = y_hat.argmax(axis=1)\n","    cmp = y_hat.type(y.dtype) == y\n","    return float(cmp.type(y.dtype).sum())\n","    \n","def evaluate_accuracy(net, data_iter):\n","    \"\"\"计算在指定数据集上模型的精度\"\"\"\n","    if isinstance(net, torch.nn.Module):\n","        net.eval()  # 将模型设置为评估模式\n","    metric = Accumulator(2)  # 正确预测数、预测总数\n","    with torch.no_grad():\n","        for X, y in data_iter:\n","            metric.add(accuracy(net(X), y), y.numel())\n","    return metric[0] / metric[1]"]},{"cell_type":"code","execution_count":223,"metadata":{},"outputs":[],"source":["class Animator:\n","    \"\"\"在动画中绘制数据\"\"\"\n","    def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,\n","                 ylim=None, xscale='linear', yscale='linear',\n","                 fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,\n","                 figsize=(3.5, 2.5)):\n","        # 增量地绘制多条线\n","        if legend is None:\n","            legend = []\n","        d2l.use_svg_display()\n","        self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize)\n","        if nrows * ncols == 1:\n","            self.axes = [self.axes, ]\n","        # 使用lambda函数捕获参数\n","        self.config_axes = lambda: d2l.set_axes(\n","            self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n","        self.X, self.Y, self.fmts = None, None, fmts\n","\n","    def add(self, x, y):\n","        # 向图表中添加多个数据点\n","        if not hasattr(y, \"__len__\"):\n","            y = [y]\n","        n = len(y)\n","        if not hasattr(x, \"__len__\"):\n","            x = [x] * n\n","        if not self.X:\n","            self.X = [[] for _ in range(n)]\n","        if not self.Y:\n","            self.Y = [[] for _ in range(n)]\n","        for i, (a, b) in enumerate(zip(x, y)):\n","            if a is not None and b is not None:\n","                self.X[i].append(a)\n","                self.Y[i].append(b)\n","        self.axes[0].cla()\n","        for x, y, fmt in zip(self.X, self.Y, self.fmts):\n","            self.axes[0].plot(x, y, fmt)\n","        self.config_axes()\n","        display.display(self.fig)\n","        display.clear_output(wait=True)"]},{"cell_type":"code","execution_count":224,"metadata":{},"outputs":[],"source":["def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):\n","    \"\"\"训练模型（定义见第3章https://zh.d2l.ai/chapter_linear-networks/index.html）\"\"\"\n","    animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.1, 3],\n","                        legend=['train loss', 'train acc', 'test acc'])\n","    for epoch in range(num_epochs):\n","        train_metrics = train_epoch_ch3(net, train_iter, loss, updater)\n","        test_acc = evaluate_accuracy(net, test_iter)\n","        animator.add(epoch + 1, train_metrics + (test_acc,))\n","    train_loss, train_acc = train_metrics\n","    print('训练遍数:', num_epochs, 'loss:', train_loss, '训练精度:', train_acc, '验证精度:', test_acc)\n","    # assert train_loss < 0.5, train_loss # loss值\n","    # assert train_acc <= 1 and train_acc > 0.7, train_acc    # 训练精度\n","    # assert test_acc <= 1 and test_acc > 0.7, test_acc    # 验证精度"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### CPU训练单连接层精度 27%"]},{"cell_type":"code","execution_count":225,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["训练遍数: 100 loss: 2.5231498740159757 训练精度: 0.3182355907222093 验证精度: 0.2738719832109129\n"]},{"data":{"image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"242.146875pt\" height=\"187.155469pt\" viewBox=\"0 0 242.146875 187.155469\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n <metadata>\n  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    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num_epochs, trainer)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### CPU训练两个连接层精度 30%"]},{"cell_type":"code","execution_count":254,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["训练遍数: 100 loss: 2.5375126427430064 训练精度: 0.36936622449771156 验证精度: 0.30220356768100737\n"]},{"data":{"image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"242.146875pt\" height=\"187.155469pt\" viewBox=\"0 0 242.146875 187.155469\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n <metadata>\n  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    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plt.rcParams['axes.unicode_minus'] = False\n","\n","    figsize = (num_cols * scale, num_rows * scale)\n","    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)\n","    axes = axes.flatten()\n","    for i, (ax, img) in enumerate(zip(axes, imgs)):\n","        if torch.is_tensor(img):\n","            # 图片张量\n","            ax.imshow(img.numpy(), **imshow_kwargs)\n","        else:\n","            # PIL图片\n","            ax.imshow(img, **imshow_kwargs)\n","        ax.axes.get_xaxis().set_visible(False)\n","        ax.axes.get_yaxis().set_visible(False)\n","        if titles:\n","            ax.set_title(titles[i])\n","    return axes"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### CPU训练模型辨认笔者的效果"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# 用训练出来的模型,分辨验证集里的笔者\n","\n","import warnings\n","warnings.filterwarnings('ignore')\n","\n","def predict_ch3(net, test_iter, n=6):\n","    \"\"\"预测标签（定义见第3章https://zh.d2l.ai/chapter_linear-networks/index.html）\"\"\"\n","    for X, y in test_iter:\n","        break\n","    trues = writer_le.inverse_transform(y)\n","    preds = writer_le.inverse_transform(net(X).argmax(axis=1))\n","    titles = [true +'\\n' + pred for true, pred in zip(trues, preds)]\n","    show_images(\n","        X[0:n].reshape((n, 28, 28)), 1, n, titles=titles[0:n], cmap='gray')\n","\n","predict_ch3(net, writer_test_iter)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 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